Data Science: Concepts and Practice (Second Edition) by Vijau Kotu and Bala Deshpande is now available. Order your copy today.
Author: Ingo Mierswa
What makes data prep so difficult and tedious? Ingo shares his thoughts on this and how RapidMiner addresses this issue with a new data prep approach.
Machine learning and data science have become an intrinsic part of business. Learn how to avoid common data science mistakes that can ruin your business.
Read through a demonstration of Turbo Prep and Auto Model by Ingo Mierswa to see how RapidMiner makes data prep and machine learning fun, fast, and simple.
One of the most frequent questions I get asked is: “Ingo, I am from Industry X and my data looks like Y and my colleague recommended to use model Z – what is your opinion on what model to use?” In this blog post, I explain a well-proven framework for model selection.
In Part 4 of this series we discuss multi-objective feature selection, which can be used for unsupervised learning & to identify best spaces for clusters.
Multi-objective optimization is great for feature selection because we can find all potentially good solutions without defining a trade-off factor.
Evolutionary algorithm is a generic optimization technique mimicking the ideas of natural evolution with the concepts of crossover, mutation, and selection.
Feature selection can greatly improve your machine learning models. Learn about it’s importance in part 1 of this blog series.
Naïve Bayes is a powerful machine learning technique. Learn more about this classifier below and make it part of your standard toolbox.